{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bet sizing 2",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOV5dOtUU+2zbK9BkSX+njA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jo-cho/advances_in_financial_machine_learning/blob/master/Bet_sizing_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bthg3s23JVw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns; sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_w5609_w735",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# sklearn\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
        "from sklearn.metrics import roc_curve, confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lefH2Ji6WyLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvpQ32OS2joc",
        "colab_type": "code",
        "outputId": "7044a299-29aa-4b02-b1d8-04f0bef10c1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5F9VG8B2oB0",
        "colab_type": "code",
        "outputId": "df9a5678-5ebe-47ae-e0aa-7e209fe45375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "# Thanks to mlfinlab!\n",
        "\n",
        "!pip install mlfinlab\n",
        "from mlfinlab import data_structures, features, filters, labeling, util, sampling"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mlfinlab\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/68/0de1f93abffe540476ee8032511913dac57189dd764ab7750225be859816/mlfinlab-0.5.0-py3-none-any.whl (149kB)\n",
            "\r\u001b[K     |██▏                             | 10kB 34.0MB/s eta 0:00:01\r\u001b[K     |████▍                           | 20kB 2.2MB/s eta 0:00:01\r\u001b[K     |██████▋                         | 30kB 3.2MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 40kB 2.1MB/s eta 0:00:01\r\u001b[K     |███████████                     | 51kB 2.6MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 61kB 3.1MB/s eta 0:00:01\r\u001b[K     |███████████████▍                | 71kB 3.6MB/s eta 0:00:01\r\u001b[K     |█████████████████▌              | 81kB 4.1MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 92kB 4.6MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 102kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 112kB 3.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 122kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 133kB 3.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 143kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 153kB 3.5MB/s \n",
            "\u001b[?25hInstalling collected packages: mlfinlab\n",
            "Successfully installed mlfinlab-0.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twYwXobOVmCG",
        "colab_type": "text"
      },
      "source": [
        "## META-LABELING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeFHQgICirXE",
        "colab_type": "text"
      },
      "source": [
        "### What is meta labeling?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEmeWgLwuKo_",
        "colab_type": "text"
      },
      "source": [
        "The first step is to train a primary model (binary classification). \n",
        "\n",
        "Second a threshold level is determined at which the primary model has a high recall, in the coded example you will find that 0.30 is a good threshold, ROC curves could be used to help determine a good level. \n",
        "\n",
        "Third the features from the first model are concatenated with the predictions from the first model, into a new feature set for the secondary model. Meta Labels are used as the target variable in the second model. \n",
        "Now fit the second model. \n",
        "\n",
        "Fourth the prediction from the secondary model is combined with the prediction from the primary model and only where both are true, is your final prediction true. I.e. if your primary model predicts a 3 and your secondary model says you have a high probability of the primary model being correct, is your final prediction a 3, else not 3."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYj34cC_8aR4",
        "colab_type": "text"
      },
      "source": [
        "Binary classification problems present a trade-off between type-I errors (false positives) and type-II errors (false negatives). In general, increasing the true positive rate of a binary classifier will tend to increase its false positive rate. The receiver operating characteristic (ROC) curve of a binary classifier measures the cost of increasing the true positive rate, in terms of accepting higher false positive rates.\n",
        "\n",
        "Meta-labeling is particularly helpful when you want to achieve higher F1-scores. First, we build a model that achieves high recall, even if the precision is not particularly high. Second, we correct for the low precision by applying meta-labeling to the positives predicted by the primary model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpnKi3rNfYf4",
        "colab_type": "code",
        "outputId": "d72944aa-0da8-4782-f2c8-fd7913481b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "from IPython.core.display import Image\n",
        "Image(filename='/content/drive/My Drive/Colab Notebooks/images/meta-labeling.png')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvEAAAKoCAYAAAD+uaDeAACAAElEQVR42uydB3xT5frHI5RSoAwR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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsrMWBgNhBgE",
        "colab_type": "text"
      },
      "source": [
        "### Read in data - dollar bars"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lypoa5PW2xIg",
        "colab_type": "code",
        "outputId": "e2c6f6cd-98a9-4261-8ffe-763669838cae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# make dollar bars from tick data\n",
        "raw_dollar_bars = data_structures.get_dollar_bars('/content/drive/My Drive/Colab Notebooks/csv/clean_IVE_tickbidask2.csv', threshold=1_000_000)\n",
        "dollar_bars = raw_dollar_bars.set_index(pd.to_datetime(raw_dollar_bars.date_time))\n",
        "dollar_bars = dollar_bars.drop(columns='date_time')\n",
        "dollar_bars = dollar_bars.reset_index().drop_duplicates(subset='date_time', keep='last').set_index('date_time')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading data in batches:\n",
            "Batch number: 0\n",
            "Returning bars \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6J1aOceRiGr7",
        "colab_type": "code",
        "outputId": "0cd3ead4-2af9-4ea5-847e-a7fa7326599e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "dollar_bars.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tick_num</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>cum_buy_volume</th>\n",
              "      <th>cum_ticks</th>\n",
              "      <th>cum_dollar_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:46:35</th>\n",
              "      <td>62</td>\n",
              "      <td>50.79</td>\n",
              "      <td>51.07</td>\n",
              "      <td>50.710</td>\n",
              "      <td>51.07</td>\n",
              "      <td>19844</td>\n",
              "      <td>13264</td>\n",
              "      <td>62</td>\n",
              "      <td>1.010425e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:53:49</th>\n",
              "      <td>108</td>\n",
              "      <td>51.07</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.050</td>\n",
              "      <td>51.14</td>\n",
              "      <td>20324</td>\n",
              "      <td>16274</td>\n",
              "      <td>46</td>\n",
              "      <td>1.038593e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:55:26</th>\n",
              "      <td>143</td>\n",
              "      <td>51.14</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.129</td>\n",
              "      <td>51.14</td>\n",
              "      <td>19622</td>\n",
              "      <td>13098</td>\n",
              "      <td>35</td>\n",
              "      <td>1.003401e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:02:52</th>\n",
              "      <td>201</td>\n",
              "      <td>51.14</td>\n",
              "      <td>51.26</td>\n",
              "      <td>51.080</td>\n",
              "      <td>51.25</td>\n",
              "      <td>22319</td>\n",
              "      <td>17454</td>\n",
              "      <td>58</td>\n",
              "      <td>1.142880e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:10:21</th>\n",
              "      <td>254</td>\n",
              "      <td>51.25</td>\n",
              "      <td>51.29</td>\n",
              "      <td>51.190</td>\n",
              "      <td>51.29</td>\n",
              "      <td>21519</td>\n",
              "      <td>14254</td>\n",
              "      <td>53</td>\n",
              "      <td>1.103149e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     tick_num   open  ...  cum_ticks  cum_dollar_value\n",
              "date_time                             ...                             \n",
              "2009-09-28 09:46:35        62  50.79  ...         62      1.010425e+06\n",
              "2009-09-28 09:53:49       108  51.07  ...         46      1.038593e+06\n",
              "2009-09-28 09:55:26       143  51.14  ...         35      1.003401e+06\n",
              "2009-09-28 10:02:52       201  51.14  ...         58      1.142880e+06\n",
              "2009-09-28 10:10:21       254  51.25  ...         53      1.103149e+06\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYC5u9X5jqc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close = dollar_bars.close"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aovQU2FLhcYp",
        "colab_type": "text"
      },
      "source": [
        "### Primary model (side) \n",
        "- Based on the simple moving average cross-over strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVZiwS-dPhzs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fast_window = 20\n",
        "slow_window = 50\n",
        "\n",
        "df = pd.DataFrame()\n",
        "df['fast_mavg'] = close.rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
        "df['slow_mavg'] = close.rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n",
        "df['side'] = np.nan\n",
        "\n",
        "long_signals = df['fast_mavg'] >= df['slow_mavg'] \n",
        "short_signals = df['fast_mavg'] < df['slow_mavg'] \n",
        "df.loc[long_signals, 'side'] = 1\n",
        "df.loc[short_signals, 'side'] = -1\n",
        "\n",
        "df['side'] = df['side'].shift(1)\n",
        "\n",
        "side = df['side']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDT_ozxvj7eU",
        "colab_type": "code",
        "outputId": "aa27ebe2-34ef-4412-a0d3-93e8d468a3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "side.value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 1.0    32190\n",
              "-1.0    26367\n",
              "Name: side, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pT2NSRXx24iv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# form a tripple-barrier\n",
        "pt_sl = [1,2]\n",
        "\n",
        "target = util.get_daily_vol(close,lookback=50)\n",
        "\n",
        "h = target.mean()*0.5\n",
        "t_events = filters.cusum_filter(close, h) # using symmetric cusum filter get t_events\n",
        "\n",
        "vertical_barrier_times = labeling.add_vertical_barrier(t_events, close, num_days=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAn2kWFFWZZv",
        "colab_type": "code",
        "outputId": "ace369b5-98c9-45a9-bb8e-054c75b8ce54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# events\n",
        "min_ret = 0.005\n",
        "\n",
        "events = labeling.get_events(close, t_events, pt_sl, target, min_ret, num_threads=3, \n",
        "                             vertical_barrier_times = vertical_barrier_times ,side_prediction = side)\n",
        "\n",
        "# label (bin)\n",
        "\n",
        "bin = labeling.get_bins(events, close)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-25 06:26:18.007255 100.0% apply_pt_sl_on_t1 done after 0.22 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-p8yHmHW0M7",
        "colab_type": "code",
        "outputId": "7e0926ea-9401-45e7-c56f-0aeaca58dfc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "bin.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ret</th>\n",
              "      <th>trgt</th>\n",
              "      <th>bin</th>\n",
              "      <th>side</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6404.000000</td>\n",
              "      <td>6410.000000</td>\n",
              "      <td>6410.000000</td>\n",
              "      <td>6404.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.000117</td>\n",
              "      <td>0.009983</td>\n",
              "      <td>0.312012</td>\n",
              "      <td>-0.059963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.013082</td>\n",
              "      <td>0.006454</td>\n",
              "      <td>0.463351</td>\n",
              "      <td>0.998279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.237752</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.007115</td>\n",
              "      <td>0.006446</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.008225</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.007684</td>\n",
              "      <td>0.011075</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.373211</td>\n",
              "      <td>0.094337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ret         trgt          bin         side\n",
              "count  6404.000000  6410.000000  6410.000000  6404.000000\n",
              "mean     -0.000117     0.009983     0.312012    -0.059963\n",
              "std       0.013082     0.006454     0.463351     0.998279\n",
              "min      -0.237752     0.005000     0.000000    -1.000000\n",
              "25%      -0.007115     0.006446     0.000000    -1.000000\n",
              "50%       0.001033     0.008225     0.000000    -1.000000\n",
              "75%       0.007684     0.011075     1.000000     1.000000\n",
              "max       0.373211     0.094337     1.000000     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-vX3SkcXYOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bin.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2chOOmHXUAZ",
        "colab_type": "code",
        "outputId": "f8795d04-2931-4bd7-c65a-a4254a228f01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "bin.side.value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0    3394\n",
              " 1.0    3010\n",
              "Name: side, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWR7vJWEWnJX",
        "colab_type": "code",
        "outputId": "c6e8c1ce-8e7f-4994-f576-6cf83d578e0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "bin.bin.value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4404\n",
              "1    2000\n",
              "Name: bin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfxFGLbAqC-t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw_data = pd.DataFrame(close)\n",
        "\n",
        "log_ret = np.log(raw_data['close']).diff()\n",
        "\n",
        "# Momentum\n",
        "raw_data['mom1'] = raw_data['close'].pct_change(periods=1)\n",
        "raw_data['mom2'] = raw_data['close'].pct_change(periods=2)\n",
        "raw_data['mom3'] = raw_data['close'].pct_change(periods=3)\n",
        "\n",
        "# Volatility\n",
        "raw_data['volatility_50'] = log_ret.rolling(window=50, min_periods=50, center=False).std()\n",
        "raw_data['volatility_31'] = log_ret.rolling(window=31, min_periods=31, center=False).std()\n",
        "raw_data['volatility_15'] = log_ret.rolling(window=15, min_periods=15, center=False).std()\n",
        "\n",
        "# Serial Correlation\n",
        "window_autocorr = 50\n",
        "\n",
        "raw_data['autocorr_1'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=1), raw=False)\n",
        "raw_data['autocorr_2'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=2), raw=False)\n",
        "raw_data['autocorr_3'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=3), raw=False)\n",
        "\n",
        "# Remove look ahead bias\n",
        "raw_data = raw_data.shift(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRbf20dNtVlS",
        "colab_type": "code",
        "outputId": "7c937945-6f70-4c8c-cae9-ca30157d243f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "raw_X = raw_data.iloc[:,1:]\n",
        "raw_X.dropna(inplace=True)\n",
        "raw_X.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mom1</th>\n",
              "      <th>mom2</th>\n",
              "      <th>mom3</th>\n",
              "      <th>volatility_50</th>\n",
              "      <th>volatility_31</th>\n",
              "      <th>volatility_15</th>\n",
              "      <th>autocorr_1</th>\n",
              "      <th>autocorr_2</th>\n",
              "      <th>autocorr_3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:42:01</th>\n",
              "      <td>-0.005646</td>\n",
              "      <td>-0.006353</td>\n",
              "      <td>-0.007579</td>\n",
              "      <td>0.002705</td>\n",
              "      <td>0.003341</td>\n",
              "      <td>0.003551</td>\n",
              "      <td>-0.051927</td>\n",
              "      <td>0.216036</td>\n",
              "      <td>-0.302316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:58:56</th>\n",
              "      <td>0.001371</td>\n",
              "      <td>-0.004283</td>\n",
              "      <td>-0.004991</td>\n",
              "      <td>0.002705</td>\n",
              "      <td>0.003353</td>\n",
              "      <td>0.003594</td>\n",
              "      <td>-0.071628</td>\n",
              "      <td>0.205664</td>\n",
              "      <td>-0.310500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:30:07</th>\n",
              "      <td>0.000391</td>\n",
              "      <td>0.001762</td>\n",
              "      <td>-0.003894</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.003347</td>\n",
              "      <td>0.003583</td>\n",
              "      <td>-0.070389</td>\n",
              "      <td>0.189256</td>\n",
              "      <td>-0.312039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:39:37</th>\n",
              "      <td>-0.003518</td>\n",
              "      <td>-0.003129</td>\n",
              "      <td>-0.001762</td>\n",
              "      <td>0.002733</td>\n",
              "      <td>0.003394</td>\n",
              "      <td>0.003663</td>\n",
              "      <td>-0.078848</td>\n",
              "      <td>0.180343</td>\n",
              "      <td>-0.242727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>-0.002942</td>\n",
              "      <td>-0.006450</td>\n",
              "      <td>-0.006062</td>\n",
              "      <td>0.002759</td>\n",
              "      <td>0.003416</td>\n",
              "      <td>0.003565</td>\n",
              "      <td>-0.048314</td>\n",
              "      <td>0.173996</td>\n",
              "      <td>-0.255830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         mom1      mom2  ...  autocorr_2  autocorr_3\n",
              "date_time                                ...                        \n",
              "2009-09-30 15:42:01 -0.005646 -0.006353  ...    0.216036   -0.302316\n",
              "2009-09-30 15:58:56  0.001371 -0.004283  ...    0.205664   -0.310500\n",
              "2009-10-01 09:30:07  0.000391  0.001762  ...    0.189256   -0.312039\n",
              "2009-10-01 09:39:37 -0.003518 -0.003129  ...    0.180343   -0.242727\n",
              "2009-10-01 09:52:26 -0.002942 -0.006450  ...    0.173996   -0.255830\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YTsGoXDwAK6",
        "colab_type": "code",
        "outputId": "08d576be-47a2-4933-ac15-722325a4e0cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "# Feature decomposition PCA\n",
        "\n",
        "n=5\n",
        "pca = PCA(n)\n",
        "pca.fit(raw_X)\n",
        "pcaX = pca.transform(raw_X)\n",
        "\n",
        "X = pd.DataFrame(index=raw_X.index)\n",
        "for i in range(n):\n",
        "  a = pd.Series(data=pcaX[:,i],index=raw_X.index,name=i)\n",
        "  X = X.join(a)\n",
        "\n",
        "X['side'] = side\n",
        "\n",
        "X.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>side</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:42:01</th>\n",
              "      <td>0.093435</td>\n",
              "      <td>-0.355411</td>\n",
              "      <td>-0.010021</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>0.003954</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:58:56</th>\n",
              "      <td>0.103315</td>\n",
              "      <td>-0.353263</td>\n",
              "      <td>-0.031490</td>\n",
              "      <td>0.005555</td>\n",
              "      <td>0.003636</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:30:07</th>\n",
              "      <td>0.093653</td>\n",
              "      <td>-0.345701</td>\n",
              "      <td>-0.042566</td>\n",
              "      <td>0.001394</td>\n",
              "      <td>0.003241</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:39:37</th>\n",
              "      <td>0.108490</td>\n",
              "      <td>-0.282626</td>\n",
              "      <td>-0.015056</td>\n",
              "      <td>0.004202</td>\n",
              "      <td>0.003426</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>0.077217</td>\n",
              "      <td>-0.294765</td>\n",
              "      <td>-0.010693</td>\n",
              "      <td>0.009079</td>\n",
              "      <td>0.003859</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            0         1         2         3         4  side\n",
              "date_time                                                                  \n",
              "2009-09-30 15:42:01  0.093435 -0.355411 -0.010021  0.011007  0.003954  -1.0\n",
              "2009-09-30 15:58:56  0.103315 -0.353263 -0.031490  0.005555  0.003636  -1.0\n",
              "2009-10-01 09:30:07  0.093653 -0.345701 -0.042566  0.001394  0.003241  -1.0\n",
              "2009-10-01 09:39:37  0.108490 -0.282626 -0.015056  0.004202  0.003426  -1.0\n",
              "2009-10-01 09:52:26  0.077217 -0.294765 -0.010693  0.009079  0.003859  -1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PBgcFJGxlI-",
        "colab_type": "code",
        "outputId": "e0cdc200-1d1a-4543-eeed-df2825e8046b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(X)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0BgIS4xtjZt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tmp_data = X.copy()\n",
        "tmp_data = tmp_data.join([bin.bin, events['t1']])\n",
        "tmp_data.dropna(inplace=True)\n",
        "\n",
        "X = tmp_data.iloc[:,:6]\n",
        "y = tmp_data[['bin']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtC_iYLvyHSz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoDiKFRqz5Dd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#avgU = sampling.get_av_uniqueness_from_triple_barrier(events, close, 3).mean()\n",
        "#max_samples = avgU[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2Kd2E7UvOtz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#clf0 = RandomForestClassifier(n_estimators=1,criterion='entropy',bootstrap = False, class_weight='balanced_subsample')\n",
        "#clf1 = BaggingClassifier(base_estimator=clf0, max_samples=max_samples, max_features=1, n_estimators=1000)\n",
        "#clf1.fit(X_train, y_train.values.ravel())\n",
        "\n",
        "#this is the method in the book"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zq1n0S81P4ue",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "4d1d0880-475d-4c8e-af8c-c616204a7cca"
      },
      "source": [
        "parameters = {'max_depth':[2, 3, 4, 5, 7],\n",
        "              'n_estimators':[10, 50, 100, 512, 1000]}\n",
        "#hyper-parameter tuning\n",
        "\n",
        "rf = RandomForestClassifier(criterion='entropy', class_weight='balanced_subsample', bootstrap=False)\n",
        "clf = GridSearchCV(rf, parameters, cv=3, scoring='roc_auc')\n",
        "clf.fit(X_train, y_train.values.ravel())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=False, ccp_alpha=0.0,\n",
              "                                              class_weight='balanced_subsample',\n",
              "                                              criterion='entropy',\n",
              "                                              max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [2, 3, 4, 5, 7],\n",
              "                         'n_estimators': [10, 50, 100, 512, 1000]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE-cvkda5wXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#y_pred = clf1.predict(X_test)\n",
        "#y_pred_prob = clf1.predict_proba(X_test)[:, 1] # 1로 예상할 확률"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9oD2-KeZSd9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "y_pred_prob = clf.predict_proba(X_test)[:, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQKHmmsd0e3F",
        "colab_type": "code",
        "outputId": "da318c47-cd1a-486c-955b-e845c69edde3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_prob)\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEcCAYAAAAydkhNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrMpBwszMqn4C4aANECdsXVgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dkg01-O5yWR",
        "colab_type": "code",
        "outputId": "d1c192cb-becd-4978-8e57-1f05d4020c9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"Accuracy = \", accuracy_score(y_test, y_pred))\n",
        "print('')\n",
        "print(\"Confusion Matrix\")\n",
        "mat = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(mat , annot=True)\n",
        "plt.xlabel('predicted label')\n",
        "plt.ylabel('true label');"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.65      0.46      0.54      1616\n",
            "         1.0       0.39      0.58      0.46       946\n",
            "\n",
            "    accuracy                           0.50      2562\n",
            "   macro avg       0.52      0.52      0.50      2562\n",
            "weighted avg       0.55      0.50      0.51      2562\n",
            "\n",
            "Accuracy =  0.5035128805620609\n",
            "\n",
            "Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEMCAYAAADOLq1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUmsGwGV6euL",
        "colab_type": "text"
      },
      "source": [
        "## Bet Sizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUfHMzWr78ar",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import bet_sizing as bs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBRzgVhM6g5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = y_pred_prob"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75bIPHHR7UUo",
        "colab_type": "code",
        "outputId": "4cd8c854-a3b2-43af-e060-b3a8359c739c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "new_data = y_test.copy()\n",
        "new_data['p'] = p\n",
        "new_data['pred'] = y_pred\n",
        "new_data"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bin</th>\n",
              "      <th>p</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.567667</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559007</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.616522</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.616522</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.574191</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559007</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559509</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.368544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.472226</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.174398</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     bin         p  pred\n",
              "date_time                               \n",
              "2015-07-09 09:56:21  0.0  0.567667   1.0\n",
              "2015-07-09 12:00:31  0.0  0.559007   1.0\n",
              "2015-07-09 15:59:28  1.0  0.616522   1.0\n",
              "2015-07-10 09:35:27  1.0  0.616522   1.0\n",
              "2015-07-10 11:48:00  1.0  0.574191   1.0\n",
              "...                  ...       ...   ...\n",
              "2019-06-12 11:59:18  0.0  0.559007   1.0\n",
              "2019-06-13 09:36:29  0.0  0.559509   1.0\n",
              "2019-06-18 09:50:22  0.0  0.368544   0.0\n",
              "2019-06-18 12:22:51  0.0  0.472226   0.0\n",
              "2019-06-19 14:01:58  1.0  0.174398   0.0\n",
              "\n",
              "[2562 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcNyLMBr6yFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test에 맞추자\n",
        "new_events = events.loc[new_data.index]\n",
        "bs_df = pd.concat([new_events, new_data],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqyt-EMU8QZZ",
        "colab_type": "code",
        "outputId": "9cc9841d-0e38-4262-a4f2-43d8d7689eac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "bs_df"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>trgt</th>\n",
              "      <th>side</th>\n",
              "      <th>pt</th>\n",
              "      <th>sl</th>\n",
              "      <th>bin</th>\n",
              "      <th>p</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>2015-07-10 09:56:51</td>\n",
              "      <td>0.008702</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.567667</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>2015-07-10 12:27:37</td>\n",
              "      <td>0.007981</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559007</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>2015-07-10 09:35:27</td>\n",
              "      <td>0.006833</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.616522</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>0.006412</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.616522</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>0.005870</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.574191</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>2019-06-13 12:16:14</td>\n",
              "      <td>0.005909</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559007</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>2019-06-14 09:48:52</td>\n",
              "      <td>0.005255</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.559509</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
              "      <td>0.005684</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.368544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>2019-06-19 12:33:36</td>\n",
              "      <td>0.007223</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.472226</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>0.005663</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.174398</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1      trgt  side  ...  bin         p  pred\n",
              "date_time                                                ...                     \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  0.008702  -1.0  ...  0.0  0.567667   1.0\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  0.007981  -1.0  ...  0.0  0.559007   1.0\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27  0.006833   1.0  ...  1.0  0.616522   1.0\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23  0.006412   1.0  ...  1.0  0.616522   1.0\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23  0.005870   1.0  ...  1.0  0.574191   1.0\n",
              "...                                 ...       ...   ...  ...  ...       ...   ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  0.005909  -1.0  ...  0.0  0.559007   1.0\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  0.005255  -1.0  ...  0.0  0.559509   1.0\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  0.005684  -1.0  ...  0.0  0.368544   0.0\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36  0.007223   1.0  ...  0.0  0.472226   0.0\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00  0.005663   1.0  ...  1.0  0.174398   0.0\n",
              "\n",
              "[2562 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8oXfxfc8fvD",
        "colab_type": "text"
      },
      "source": [
        "### bet size probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2ei_4N38huh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_prob = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMjvm6oW87-v",
        "colab_type": "code",
        "outputId": "cebb2240-7b08-4e81-d26d-766b8882716a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "m_prob"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date_time\n",
              "2015-07-09 09:56:21    0.108646\n",
              "2015-07-09 12:00:31    0.094602\n",
              "2015-07-09 15:59:28    0.189392\n",
              "2015-07-10 09:35:27    0.189392\n",
              "2015-07-10 11:48:00    0.119269\n",
              "                         ...   \n",
              "2019-06-12 11:59:18    0.094602\n",
              "2019-06-13 09:36:29    0.095414\n",
              "2019-06-18 09:50:22   -0.214761\n",
              "2019-06-18 12:22:51   -0.044367\n",
              "2019-06-19 14:01:58   -0.609154\n",
              "Name: signal, Length: 2562, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvmeDg5u9Qhl",
        "colab_type": "text"
      },
      "source": [
        "average"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWBHmphD9Tdq",
        "colab_type": "code",
        "outputId": "32a81c61-2ea9-487e-d1e4-7bcf15b82919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "m_prob_avg = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3, average_active=True)\n",
        "m_prob_avg"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-25 06:41:56.824596 100.0% mp_avg_active_signals done after 0.17 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-07-09 09:56:21    0.108646\n",
              "2015-07-09 12:00:31    0.101624\n",
              "2015-07-09 15:59:28    0.130880\n",
              "2015-07-10 09:35:27    0.130880\n",
              "2015-07-10 09:56:51    0.141997\n",
              "                         ...   \n",
              "2019-06-18 12:22:51   -0.129564\n",
              "2019-06-19 09:57:29   -0.044367\n",
              "2019-06-19 12:33:36    0.000000\n",
              "2019-06-19 14:01:58   -0.609154\n",
              "2019-06-20 09:30:00    0.000000\n",
              "Length: 3830, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AArwEUGZ9USQ",
        "colab_type": "code",
        "outputId": "96a65938-90ae-4a0f-d486-eba63c3b2351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "m_prob_discrete = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3, average_active=True, step_size=0.01)\n",
        "m_prob_discrete"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-25 06:42:07.083783 33.33% mp_avg_active_signals done after 0.16 minutes. Remaining 0.33 minutes.\r2020-01-25 06:42:07.089037 66.67% mp_avg_active_signals done after 0.16 minutes. Remaining 0.08 minutes.\r2020-01-25 06:42:07.440236 100.0% mp_avg_active_signals done after 0.17 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-07-09 09:56:21    0.11\n",
              "2015-07-09 12:00:31    0.10\n",
              "2015-07-09 15:59:28    0.13\n",
              "2015-07-10 09:35:27    0.13\n",
              "2015-07-10 09:56:51    0.14\n",
              "                       ... \n",
              "2019-06-18 12:22:51   -0.13\n",
              "2019-06-19 09:57:29   -0.04\n",
              "2019-06-19 12:33:36    0.00\n",
              "2019-06-19 14:01:58   -0.61\n",
              "2019-06-20 09:30:00    0.00\n",
              "Length: 3830, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Islsd4M729B",
        "colab_type": "text"
      },
      "source": [
        "### bet size reserve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIgIVhFu70XZ",
        "colab_type": "code",
        "outputId": "a199f7b0-1172-41db-bccf-622a17d6a3bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "m_reserve = bs.bet_sizing.bet_size_reserve(events_t1=bs_df['t1'], sides=bs_df.side, return_parameters=True, fit_runs=50)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "|#########################| Completed 50 of 50 fitting rounds."
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7T72qjx75c5",
        "colab_type": "code",
        "outputId": "f18d522a-541f-454e-c5bf-d68044538d40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "m_reserve[0]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>side</th>\n",
              "      <th>active_long</th>\n",
              "      <th>active_short</th>\n",
              "      <th>c_t</th>\n",
              "      <th>bet_size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>2015-07-10 09:56:51</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.151861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>2015-07-10 12:27:37</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>-0.305167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>2015-07-10 09:35:27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.151861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.151861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.213739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>2019-06-13 12:16:14</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.405732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>2019-06-14 09:48:52</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>-0.305167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.151861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>2019-06-19 12:33:36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.213739</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1  side  ...  c_t  bet_size\n",
              "date_time                                      ...               \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  -1.0  ...   -1 -0.151861\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  -1.0  ...   -2 -0.305167\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27   1.0  ...   -1 -0.151861\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23   1.0  ...   -1 -0.151861\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23   1.0  ...    1  0.213739\n",
              "...                                 ...   ...  ...  ...       ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  -1.0  ...    2  0.405732\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  -1.0  ...   -2 -0.305167\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  -1.0  ...   -1 -0.151861\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36   1.0  ...    0  0.000000\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00   1.0  ...    1  0.213739\n",
              "\n",
              "[2562 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6vfUje3CBPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BetSizeReserve = m_reserve[0].bet_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NucjJ_DN9vl_",
        "colab_type": "code",
        "outputId": "878f87b0-18fb-4aea-a02a-2c41c557b7ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "m_reserve[1]\n",
        "#parameter for two Gaussian mixture"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mu_1': -1.44952,\n",
              " 'mu_2': 3.71153,\n",
              " 'p_1': 0.92797,\n",
              " 'sigma_1': 4.10773,\n",
              " 'sigma_2': 2.85437}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dfB2jjncf5r",
        "colab_type": "text"
      },
      "source": [
        "### bet size budget"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2NP_MKf_5s7",
        "colab_type": "code",
        "outputId": "a6bd40f1-20cd-416b-c071-c486ca57e0eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "m_budget = bs.bet_sizing.bet_size_budget(bs_df.t1, bs_df.side)\n",
        "m_budget"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>side</th>\n",
              "      <th>active_long</th>\n",
              "      <th>active_short</th>\n",
              "      <th>bet_size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>2015-07-10 09:56:51</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>2015-07-10 12:27:37</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>2015-07-10 09:35:27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.107143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>2019-06-13 12:16:14</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.178571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>2019-06-14 09:48:52</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>2019-06-19 12:33:36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1  side  ...  active_short  bet_size\n",
              "date_time                                      ...                        \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  -1.0  ...             1 -0.035714\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  -1.0  ...             2 -0.071429\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27   1.0  ...             2  0.000000\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23   1.0  ...             2  0.000000\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23   1.0  ...             1  0.107143\n",
              "...                                 ...   ...  ...           ...       ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  -1.0  ...             1  0.178571\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  -1.0  ...             2 -0.071429\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  -1.0  ...             1 -0.035714\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36   1.0  ...             1  0.035714\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00   1.0  ...             0  0.071429\n",
              "\n",
              "[2562 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04NQKAnVBjKt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BetSizeBudget = m_budget.bet_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAjfISA7_qvn",
        "colab_type": "text"
      },
      "source": [
        "overview"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_2osvUt941C",
        "colab_type": "code",
        "outputId": "7b1ba142-d82c-4db0-f93d-36414d15cc5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "bet_size_0 = pd.DataFrame({'BetSizeProb':m_prob,'BetSizeProbAvg':m_prob_avg,'BetSizeProbDiscrete':m_prob_discrete})\n",
        "bet_size_1 = bet_size_0.join(BetSizeReserve)\n",
        "bet_size = bet_size_1.join(BetSizeBudget,rsuffix='2')\n",
        "bet_size = bet_size.rename(columns={'bet_size':'BetSizeReserve', 'bet_size2':'BetSizeBudget'})\n",
        "bet_size"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BetSizeProb</th>\n",
              "      <th>BetSizeProbAvg</th>\n",
              "      <th>BetSizeProbDiscrete</th>\n",
              "      <th>BetSizeReserve</th>\n",
              "      <th>BetSizeBudget</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>0.108646</td>\n",
              "      <td>0.108646</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.151861</td>\n",
              "      <td>-0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>0.094602</td>\n",
              "      <td>0.101624</td>\n",
              "      <td>0.10</td>\n",
              "      <td>-0.305167</td>\n",
              "      <td>-0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>0.189392</td>\n",
              "      <td>0.130880</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.151861</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>0.189392</td>\n",
              "      <td>0.130880</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.151861</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:56:51</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.141997</td>\n",
              "      <td>0.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>-0.044367</td>\n",
              "      <td>-0.129564</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 09:57:29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.044367</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 12:33:36</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>-0.609154</td>\n",
              "      <td>-0.609154</td>\n",
              "      <td>-0.61</td>\n",
              "      <td>0.213739</td>\n",
              "      <td>0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-20 09:30:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3830 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BetSizeProb  BetSizeProbAvg  ...  BetSizeReserve  BetSizeBudget\n",
              "2015-07-09 09:56:21     0.108646        0.108646  ...       -0.151861      -0.035714\n",
              "2015-07-09 12:00:31     0.094602        0.101624  ...       -0.305167      -0.071429\n",
              "2015-07-09 15:59:28     0.189392        0.130880  ...       -0.151861       0.000000\n",
              "2015-07-10 09:35:27     0.189392        0.130880  ...       -0.151861       0.000000\n",
              "2015-07-10 09:56:51          NaN        0.141997  ...             NaN            NaN\n",
              "...                          ...             ...  ...             ...            ...\n",
              "2019-06-18 12:22:51    -0.044367       -0.129564  ...        0.000000       0.035714\n",
              "2019-06-19 09:57:29          NaN       -0.044367  ...             NaN            NaN\n",
              "2019-06-19 12:33:36          NaN        0.000000  ...             NaN            NaN\n",
              "2019-06-19 14:01:58    -0.609154       -0.609154  ...        0.213739       0.071429\n",
              "2019-06-20 09:30:00          NaN        0.000000  ...             NaN            NaN\n",
              "\n",
              "[3830 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSK9HdDhRJIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bet_size.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzyV4XujNPeK",
        "colab_type": "text"
      },
      "source": [
        "Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLUEVfuhKMbc",
        "colab_type": "code",
        "outputId": "8b574a38-f2db-4174-f4e4-6777afdf2f20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "bet_size_3 = bet_size.drop(['BetSizeProbAvg','BetSizeProbDiscrete'],axis=1)\n",
        "ax.plot(bet_size_3[-100:])\n",
        "\n",
        "ax.legend(labels=bet_size_3.columns,loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_title(\"Bet Size 100 points\", fontsize=14)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size 100 points')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAF5CAYAAADaho1nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaSu4zBICuFN",
        "colab_type": "code",
        "outputId": "351b0f70-96c3-43d6-8f0a-728f1e62dc1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "bet_size_4 = bet_size.iloc[:,:3]\n",
        "ax.plot(bet_size_4[-100:])\n",
        "\n",
        "ax.legend(labels=bet_size_4.columns,loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_title(\"Bet Size Prob 100 points\", fontsize=14)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Prob 100 points')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAF5CAYAAAA4WpYeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERESuz/AyBHB2Hxa9jxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LRuhbKtNx2N",
        "colab_type": "code",
        "outputId": "98e7b2ba-a3ef-4bd4-830c-b36215596951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "for c in ['BetSizeProb','BetSizeReserve', 'BetSizeBudget']:\n",
        "    ax.hist(bet_size_3[c],  label=c, alpha=0.6, bins=100)\n",
        "ax.legend(loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_xlabel(\"Bet Size, $m_t$\", fontsize=12)\n",
        "ax.set_ylabel(\"Value count\", fontsize=12)\n",
        "ax.set_title(\"Bet Size Distribution\", fontsize=14)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAGNCAYAAABOuTbmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0LAHHJkNaa1",
        "colab_type": "code",
        "outputId": "03a793ac-f705-4893-c5f8-e8e6f3752441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "for c in ['BetSizeProb','BetSizeProbAvg', 'BetSizeProbDiscrete']:\n",
        "    ax.hist(bet_size_4[c],  label=c, alpha=0.6, bins=100)\n",
        "ax.legend(loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_xlabel(\"Bet Size, $m_t$\", fontsize=12)\n",
        "ax.set_ylabel(\"Value count\", fontsize=12)\n",
        "ax.set_title(\"Bet Size Distribution\", fontsize=14)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAGNCAYAAABOuTbmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK_HVmubO9hn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7YIGyDtPH92",
        "colab_type": "text"
      },
      "source": [
        "## REFERENCE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ym6rl-oGPJIT",
        "colab_type": "text"
      },
      "source": [
        "Advances in Financial Machine Learning, Lopez de Prado\n",
        "\n",
        "https://github.com/hudson-and-thames/research"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPcMO2z3PUCY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
